2019
DOI: 10.1177/1550147719891406
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Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks

Abstract: The development of wireless acoustic sensor networks has driven the use of acoustic signals for target monitoring. Most monitoring applications require continuous network connectivity and data transfers, which can rapidly exhaust nodes’ energy. Consequently, sensors must collaborate in an adequate architecture to perform target recognition and localization tasks and then to send the results to a remote server with a reduced data volume. The design of an energy-efficient scheme that achieves acoustic target rec… Show more

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Cited by 3 publications
(12 citation statements)
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References 39 publications
(113 reference statements)
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“…(a) Single acoustic target localization algorithms: Most of the energy-based acoustic source localization research works have utilized least square (LS) methods such as weighted LS [14], unconstrained LS-quadratic-term elimination (QE) [15] [16] [10], semidefinite programming (SDP) [17], projection onto convex (POCs) [18], and a diffusionbased projection [19]. solutions are proposed using either expectation maximization, multiresolution projection [9], alternating projection search method [24], or sparse approximation [25].…”
Section: Related Workmentioning
confidence: 99%
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“…(a) Single acoustic target localization algorithms: Most of the energy-based acoustic source localization research works have utilized least square (LS) methods such as weighted LS [14], unconstrained LS-quadratic-term elimination (QE) [15] [16] [10], semidefinite programming (SDP) [17], projection onto convex (POCs) [18], and a diffusionbased projection [19]. solutions are proposed using either expectation maximization, multiresolution projection [9], alternating projection search method [24], or sparse approximation [25].…”
Section: Related Workmentioning
confidence: 99%
“…More specifically, Least Square estimators are sensitive to local optima, saddle points, whereas Maximum Likelihood estimators require costly iterative search and good initial region to be satisfied; otherwise, divergence and local convergence problems would happen [5]. Additionally, the prior published studies have not examined the performance of their proposed algorithms in terms of energy consumption except in [10], where localization accuracy is severely sacrificed for obtaining a low energy-cost approach. Thus, an efficient but lightweight energy-based acoustic source localization solution still needs further research.…”
Section: Introductionmentioning
confidence: 99%
“…Due to the cost-intensive algorithms used in the domain of signal processing, only a little attention was given to acoustic-based target recognition in WASNs [3,4,[10][11][12][13][14]19]. In [14], two spectral features were used in the proposed approach for binary classification of the sound to either speech or music.…”
Section: Related Workmentioning
confidence: 99%
“…Since the background noise level might vary, the silence threshold for each sound record is calculated adaptively based on the signal's average energy. In our approach, we defined the silent frame as a frame whose Rootmean-square value (RMS i ) is below 10% of the long-term average signal energy [19]. Figure 2a-c shows the result of a sound record's silence removal algorithm in the dataset.…”
Section: Signal Preprocessingmentioning
confidence: 99%
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